基于VIIRS数据的南黄海区域激光测深性能评估

机载激光测深技术因其特有的优势备受关注,但现阶段其探测能力有限,为解决其适用范围问题,本文利用VIIRS(Visible Infrared Imaging Radiometer Suite)数据对南黄海区域的激光测深性能进行评估,首先利用外业实测数据和卫星影像反演南黄海区域的490 nm波段漫衰减系数,其次建立490 nm和532 nm波段漫衰减系数之间的线性关系,最后以CZMIL(Coastal Zone Mapping and Imaging LiDAR)系统为例评估了南黄海区域激光测深能力,并给出了江苏沿岸海域CZMIL系统的适用范围。研究结果表明:CZMIL系统在南黄海区域的最大测深能力范围为0.5~40 m;理论最低潮面时,江苏沿海海域CZMIL系统的适用范围约为4 700 km²。     

An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

Many real-world applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellite-based monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat-8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modified-ESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsat-like images from different algorithms (STARFM, modified-STARFM, ESTARFM, modified-ESTARFM) indicate that the modified-ESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modified-ESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.     

POI与NPP/VIIRS夜光数据空间耦合关系下的城市空间结构分析——以武汉市主城区为例

城市兴趣点（POI）和夜光遥感影像能够直观反映城市社会经济等实体要素的空间分布特征,在城市空间结构研究中发挥着重要作用。本文首先选取长江中游城市群的典型城市代表——武汉市作为研究区,选用研究区2016年POI和NPP/VIIRS夜光遥感数据作为基础研究数据,采用GIS分析工具对POI数据进行了空间核密度分析;然后分别对POI核密度分析结果和NPP/VIIRS夜光遥感数据进行了空间网格化处理;最后采用双因素制图和栅格叠加分析方法对两类数据的空间耦合关系进行了探讨,并在此基础上进一步分析了城市空间结构特征。研究表明,武汉市POI数据和夜光遥感数据的空间耦合性整体较好,空间耦合相一致区域占比为82.15%;但POI数据和夜光遥感数据的空间耦合性在长江沿岸地区也存在部分差异,如硚口区、汉阳区夜光遥感数据和POI数据多以低—中的空间耦合模式为主,而青山区、武昌区和汉口区则多以中—低的空间耦合模式为主。武汉市作为中原城市群的核心城市之一,其城市内部空间结构与长江经济带发展关联密切,通过对POI和夜光遥感数据的空间耦合关系探讨,能够对武汉市空间实体要素的空间结构特征有更加深入的了解。本文结果可为沿江城市内部空间结构的研究提供一种崭新的视角。     

大都市城乡融合区空间演进及内在关联性测度——基于武汉市夜间灯光数据

大都市城乡融合区时空演变及其内在空间关联性研究对避免城市低效蔓延式发展、实现空间结构协同和精明增长有重大意义。论文以武汉市城乡融合区为研究对象,基于NPP/VIIRS夜间灯光强度表征都市要素配置及运行效率和效益,利用引力模型测度2016—2018年各城乡融合区空间关联强度,分析各城乡融合区空间联系势能时空演变趋势和社会经济区位度变化。研究结果表明：① 武汉市城乡融合区面积在2016—2018年间扩大了28.10%,小斑块区域逐渐整合,区域发展连续性增强,总体向西北方向扩展最为显著;② 武汉市城乡融合区之间的空间联系网络结构整体上趋向“多中心”分布,这一过程源于各城乡融合区的经济发展水平、城镇化发展规模和交通通达性变化,东湖新技术开发区、中法武汉生态示范城、武汉临空经济区等在此过程中是各城乡融合区的区域发展引擎;③ 武汉市城乡融合区空间联系强度整体上逐年上升,空间联系整体格局从武洪区和江夏区的“大小中心”结构过渡到江夏区—武洪区—蔡汉区—江黄区—江硚东区的“外围圈层式”结构,但洪山区和江夏区作为武汉市城乡融合区中心区域,未能形成多方向空间辐射力和吸引力,对其他地区的带动明显不足;④ 各城乡融合区社会经济区位度变化显示,现行武汉市都市发展区规划实施成效在北部、西部和东北部城乡融合区较为显著。     

VIIRS和AVHRR海表温度场空间精细度估算

对上层海洋次中尺度过程研究至关重要的卫星海表温度(Sea Surface Temperature,SST)场的空间精细度一直未受到足够重视。由于卫星SST产品反演噪声的影响和实测数据的缺乏等原因,目前对卫星SST场空间精细度的研究受到较大限制。本研究开发了一套估算卫星SST场空间精细度的方法,将Suomi National Polar-orbiting Partnership卫星Visible Infrared Imager Radiometer Suite(Suomi-NPP/VIIRS)和NOAA-15卫星Advanced Very High Resolution Radiometer(NOAA-15/AVHRR)Level-2 SST场的空间能量谱与长时间在同一航线反复观测的高空间分辨率实测海温数据的空间能量谱进行了比较。研究发现VIIRS SST场夜间沿扫描方向在1.5~50 km尺度对海表温度空间能量的分布特征和变化趋势描述准确,日变化导致VIIRS白天场次中尺度空间谱能量相对夜晚有所增加。AVHRR SST场空间谱能量在次中尺度相比VIIRS有较大升高。     

Landsat/OLI与夜间灯光数据在提取城市不透水面中的精度差异分析

不透水面作为反映城市发展程度和表征城市生态环境的重要指标,在城市化研究中成为重要的数据源。当前,不透水面信息的获取通常基于遥感数据来开展,包括不同分辨率的遥感数据。这些遥感数据在高精度提取城市不透水面的能力具有较大的差异,会因尺度不同而带来提取精度的偏差。因此,理解不同遥感数据源在不透水面提取上的差异尤为重要。本文利用Landsat/OLI光谱数据和VIIRS/DNB夜间灯光数据分别采用线性光谱混合分析法和大尺度不透水面指数法提取珠江三角洲研究区的不透水面信息,并从不透水面总体精度、不同密度精度对比分析2类数据源提取不透水面的差异。结果表明：① Landsat/OLI和VIIRS/DNB两者提取不透水面的总体精度差异不大,Landsat/OLI提取不透水面的精度总体上略高于VIIRS/DNB。2种数据提取不透水面的均方根误差RMSE分别是0.18和0.21,系统误差SE分别是0.12和0.13,决定系数R ²分别是0.76和0.67。② Landsat/OLI和VIIRS/DNB数据对不同密度不透水面分布区域的提取能力不同：VIIRS/DNB在低密度不透水面区域提取精度高于Landsat/OLI;而Landsat/OLI在中、高密度不透水面区域提取精度均高于VIIRS/DNB。通过2种数据提取精度差异的对比,以期为不同密度的不透水面分布区域提取找到最佳尺度的数据源,提高不透水面提取的效率和精度。     

A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery

ABSTRACT

Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test.     

融合多源空间数据的城镇人口分布估算

精细尺度的城镇人口空间分布是分析人类-资源-环境相互关系的重要指标。本文提出了一种融合地理空间大数据和高分辨率遥感数据估计精细尺度城镇人口分布的方法。通过对比各指标与人口相关性,选取R2>0.7的建筑面积、到道路距离、夜间灯光强度、商服中心、EAHSI指数、幼儿园、公园、小学、加油站、医院、公交车站、长途汽车站作为影响人口分布的变量因子。结合城市功能区数据确定人口分布区域,利用随机森林模型对宁波市2018年人口数据进行了500 m格网空间化,从而得出宁波市城镇人口空间分布图。最后,基于随机森林模型的变量因子重要性分析宁波市人口空间分布的影响因素。研究结果表明,本文所提出的城镇人口分布模型在街道尺度的估算精度为81.2%,平均相对误差MRE为0.29、RMSE为3279.89;网格级别的MRE为17.16,RMSE为1149.9,因此模型能精确地反演城镇内部街道人口分布信息。通过对变量因子重要性进行比较,发现建筑面积重要性约为0.22,对宁波市人口估算影响最大;到道路的距离、夜间灯光强度、商服中心、EAHSI（Elevation-Adjusted Human Settlement Index）、幼儿园、公园对宁波市人口估算具有重要作用。本文在格网级别进行的人口分布精度验证对于研究城市精细人口分布具有重大意义。     

“Ghost cities” identification using multi-source remote sensing datasets: A case study in Yangtze River Delta

Drastic urbanization has taken place in China during the last two decades. Recently, a considerable number of “ghost cities” have emerged due to the extensive and unreasonable urban expansion which far exceeds the practical demand. In order to investigate “ghost cities”, we proposed a feasible framework by utilizing multi-source remote sensing datasets, including nighttime light imagery, land cover type products and population grid. After eliminating blooming effect of nighttime imagery by a proposed modified optimal threshold method (MOTM) and extracting built-up area from land cover type products, we developed a “ghost city” index (GCI) to quantify and evaluate the intensity of “ghost city” phenomenon in Yangtze River Delta at county/district level. The GCI was established according to the intrinsic features of “ghost cities”, comprising three criteria: consistency of lit area and built-up area, illumination intensity and population density. Then, we explored the spatial pattern of “ghost cities” of different GCI categories and the ternary contour was applied to demonstrate the key factor among three criteria. Our finding implies that “ghost cities” are prominently spatial clustered. Meanwhile, counties and new development zones have higher risk of suffering from the phenomenon, while capital cities and municipal cities have an alleviative effect for ambient regions. Besides, regions with higher intensity of the phenomenon tend to have less balanced composition among three criteria. Our results show good consistency with previous reports and studies, providing a more objective and spatial explicit insight into the “ghost city” phenomenon. Our findings do not only prove the capability of monitoring “ghost cities” using remote sensing data, but would also be beneficial to urban planning and regional sustainable development.     

NPP卫星VIIRS微光资料反演夜间PM2.5质量浓度

为研究城市夜间PM_(2.5)质量浓度,利用NPP卫星上可见光红外成像辐射套件(VIIRS)DNB通道的微光辐射数据,以辐射传输理论为基础,建立了夜间城市灯光辐射强度与地面表层PM_(2.5)质量浓度的关系,并基于支持向量机方法建立了夜间城市PM_(2.5)质量浓度反演模型。以北京市作为研究对象,选取2015-03—2015-05期间无月、无云且晴朗夜空条件下4个PM_(2.5)监测站点的微光辐射数据与时空匹配的PM_(2.5)质量浓度数据对模型进行验证。研究结果表明:夜间城市灯光辐射强度与地面表层PM_(2.5)质量浓度呈现负相关性,相关系数最高的定陵站点达到–0.83。基于支持向量机方法建立的PM_(2.5)反演模型获得的PM_(2.5)质量浓度与实际PM_(2.5)质量浓度的相关系数达到0.95,反演结果较优,为进一步大范围监测PM_(2.5)质量浓度空间分布以及改善城市夜间空气质量状况评估方法提供了可行性参考。